CN113505890A - Ship manufacturing process generation method and device - Google Patents
Ship manufacturing process generation method and device Download PDFInfo
- Publication number
- CN113505890A CN113505890A CN202110863379.9A CN202110863379A CN113505890A CN 113505890 A CN113505890 A CN 113505890A CN 202110863379 A CN202110863379 A CN 202110863379A CN 113505890 A CN113505890 A CN 113505890A
- Authority
- CN
- China
- Prior art keywords
- parameter item
- data
- typical
- correlation value
- typical process
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 621
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 69
- 238000010276 construction Methods 0.000 claims abstract description 18
- 238000005520 cutting process Methods 0.000 claims description 15
- 238000001514 detection method Methods 0.000 claims description 12
- 238000000576 coating method Methods 0.000 claims description 10
- 238000003466 welding Methods 0.000 claims description 9
- 238000003754 machining Methods 0.000 claims description 3
- 230000000399 orthopedic effect Effects 0.000 claims description 3
- 238000013461 design Methods 0.000 abstract description 14
- 230000000875 corresponding effect Effects 0.000 description 21
- 238000010586 diagram Methods 0.000 description 6
- 239000000463 material Substances 0.000 description 6
- 238000004364 calculation method Methods 0.000 description 4
- 239000011248 coating agent Substances 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 239000003973 paint Substances 0.000 description 2
- 239000002131 composite material Substances 0.000 description 1
- 230000002596 correlated effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000011112 process operation Methods 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 239000002023 wood Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/022—Knowledge engineering; Knowledge acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/02—Knowledge representation; Symbolic representation
- G06N5/022—Knowledge engineering; Knowledge acquisition
- G06N5/025—Extracting rules from data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Strategic Management (AREA)
- Software Systems (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Computational Linguistics (AREA)
- Entrepreneurship & Innovation (AREA)
- Data Mining & Analysis (AREA)
- Economics (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Artificial Intelligence (AREA)
- Game Theory and Decision Science (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- General Factory Administration (AREA)
Abstract
The invention discloses a ship manufacturing process generation method and a ship manufacturing process generation device. The ship manufacturing process generation method comprises the following steps: receiving process demand data, wherein the process demand data comprises demand process types and demand process parameter items; selecting typical process data with the typical process type same as the required process type from a typical process library, wherein the typical process data comprises the typical process type, typical process parameter items and typical process implementation data; calculating a final correlation value according to the typical process parameter item and the required process parameter item; and determining target process data according to the final correlation value. The scheme of the invention improves the efficiency of process design and the stability of ship construction quality.
Description
Technical Field
The embodiment of the invention relates to a ship manufacturing technology, in particular to a ship manufacturing process generating method and device.
Background
In the ship building process, the ship building process is complicated and complicated due to the characteristics of large size, complex structure, precise system and the like. The process knowledge involved in the process design is multiple and complicated, so that the current process design efficiency is low, the process refinement degree is not enough, and the influence on the ship building quality is large, so that the rapid determination of the refined ship manufacturing process has important significance on the ship building.
Currently, ship manufacturing process design depends on personal experience knowledge of designers to a large extent, and a large amount of experience knowledge is dispersed in the mind of more designers in related fields, so that the reusability and the shareability of process knowledge are poor. Meanwhile, the process design period is long, the efficiency is low, meanwhile, the process designed manually is extensive, construction personnel are required to determine specific parameters, and different construction personnel determine specific parameter values in the extensive process range according to own subjective consciousness, so that the implementation parameters of each process are basically different, and the manufacturing quality stability is poor.
Disclosure of Invention
The invention provides a ship manufacturing process generation method and a ship manufacturing process generation device, which are used for improving the efficiency of process design and the stability of ship construction quality.
In a first aspect, an embodiment of the present invention provides a ship manufacturing process generation method, where the ship manufacturing process generation method includes:
receiving process demand data, wherein the process demand data comprises demand process types and demand process parameter items;
selecting typical process data with the typical process type same as the required process type from a typical process library, wherein the typical process data comprises the typical process type, typical process parameter items and typical process implementation data;
calculating a final correlation value according to the typical process parameter item and the required process parameter item;
and determining target process data according to the final correlation value.
Optionally, determining whether the typical process data is target process data according to the final correlation value includes:
determining the typical process data with the final correlation value larger than a preset threshold value as the target process data.
Optionally, calculating a final correlation value according to the typical process parameter item and the required process parameter item includes:
if the required process parameter item is a digital type, calculating a first correlation value of the required process parameter item according to a first formula, wherein the first formula is that ki is 1- (| Ai-Bi |/Bi), ki is the first correlation value of the ith required process parameter item, Ai is the numerical value of the ith typical process parameter item, Bi is the numerical value of the ith required process parameter item, i is a positive integer, and i corresponds to the required process parameter item one to one;
if the required process parameter item is of a character type, judging whether the content of the required process data parameter item is completely consistent with that of the corresponding typical process parameter item, if so, the first correlation value is 1, otherwise, the first correlation value is 0;
calculating a final correlation value according to the first correlation value and a second formula
And K is the final correlation value, and xi is a preset correlation coefficient of the ith parameter item.
Optionally, calculating a final correlation value according to the typical process parameter item and the required process parameter item includes:
determining special parameter items in the required process parameter items;
and when the contents of the special parameter item and the corresponding typical process parameter item are the same, calculating a final correlation value according to the typical process parameter item and the required process parameter item.
Optionally, before receiving the process requirement data, the method further includes:
receiving the typical process data input by a process worker;
and saving the typical process data to form the typical process library.
Optionally, before receiving the typical process data entered by a process person, the method further includes:
collecting process data in the ship production construction process;
collecting quality detection data corresponding to the process data;
storing the process data and the corresponding quality detection data according to process types in a classified manner to form a process database;
and extracting the process data with the best quality detection data under each process type from the process database to serve as the typical process data to form the typical process database.
Optionally, the typical process type and the required process type each include: the method comprises a cutting process, a machining forming process, an assembling process, a welding process, an orthopedic process and a coating process.
In a second aspect, an embodiment of the present invention further provides a ship manufacturing process generating apparatus, where the ship manufacturing process generating apparatus includes: the system comprises a process demand data receiving module, a typical process data selecting module, a final correlation value calculating module and a target process data determining module; the process demand data receiving module is used for receiving process demand data, wherein the process demand data comprises a demand process type and a demand process parameter item; the typical process data selection module is used for selecting typical process data with the typical process type being the same as the required process type from a typical process library, wherein the typical process data comprises the typical process type, typical process parameter items and typical process implementation data; the final correlation value calculating module is used for calculating a final correlation value according to the typical process parameter item and the required process parameter item; and the target process data determining module is used for determining target process data according to the final correlation value.
Optionally, the final correlation value calculating module includes: the correlation value calculation unit is used for calculating a first correlation value of the required process parameter item according to a first formula if the required process parameter item is a digital type, wherein the first formula is that ki is 1- (| Ai-Bi |/Bi), ki is a first correlation value of the ith required process parameter item, Ai is a numerical value of the ith required process parameter item, Bi is a numerical value of the ith typical process parameter item, i is a positive integer, and i corresponds to the required process parameter item one to one; and is used for judging whether the required process parameter item is completely consistent with the content of the corresponding typical process parameter item if the required process parameter item is of a character type, if so, determining that the first correlation value is 1, otherwise, determining that the first correlation value is 0; a first final correlation value calculating unit for calculating a final correlation value based on the first correlation value and a second formula
And K is the final correlation value, and xi is a preset correlation coefficient of the ith parameter item.
Optionally, the final correlation value calculating module includes: a special parameter item determining unit and a second final correlation value calculating unit; the special parameter item determining unit is used for determining a special parameter item in the required process parameter items; the second final correlation value calculating unit is used for calculating a final correlation value according to the typical process parameter item and the required process parameter item when the contents of the special parameter item and the corresponding typical process parameter item are the same.
According to the ship manufacturing process generation method and device provided by the embodiment of the invention, the correlation degree between the typical process and the required process is correspondingly analyzed according to the actual requirements of process personnel, and then the target process is determined according to the final correlation value, so that the target typical process is accurately searched and determined, the dependence of process design on personal experience is reduced, the typical process implementation data can directly guide the operation of constructors, the ship construction quality is ensured, and the efficiency of process design and the stability of the ship construction quality are improved.
Drawings
Fig. 1 is a flowchart of a ship manufacturing process generation method according to an embodiment of the present invention;
FIG. 2 is a flow chart of another ship manufacturing process generation method provided by the embodiment of the invention;
FIG. 3 is a flow chart of a method for generating a ship manufacturing process according to an embodiment of the present invention;
FIG. 4 is a flow chart of a method for generating a ship manufacturing process according to an embodiment of the present invention;
FIG. 5 is a flow chart of an exemplary process library formation method provided in accordance with an embodiment of the present invention;
FIG. 6 is a flow chart of another exemplary process library formation method provided in accordance with an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a ship manufacturing process generating device according to an embodiment of the present invention;
FIG. 8 is a schematic structural diagram of another ship manufacturing process generation device provided by the embodiment of the invention;
fig. 9 is a schematic structural diagram of another ship manufacturing process generation device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
The embodiment of the invention provides a ship manufacturing process generation method. Fig. 1 is a flowchart of a ship manufacturing process generation method according to an embodiment of the present invention, and referring to fig. 1, the ship manufacturing process generation method includes:
s101, receiving process requirement data.
Specifically, the process demand data includes demand process types and demand process parameter items; the process type may be a type obtained by classifying a required process according to different flows in the ship manufacturing process, and the process type may be an assembly process, a coating process, and other ship manufacturing process types. The process parameter items are the type of an implementation object, the implementation required precision and other parameters which embody the characteristics of the process types, which need to be mastered when each process type is implemented, the process parameter items of different process types can be different, the process parameter items of the same process type are the same, illustratively, the process parameter items of the welding process can be plate thickness, material, groove and any other parameter which can embody the information of the welding object, the process parameter items of the cutting process can be material, cutting thickness, cutting precision and any other parameter which can embody the cutting object and the requirements, and the process parameter items of the coating process can be segment numbers, the regions in the segment numbers, paint types and any parameter which can embody the coating object and the requirements. In the process of ship manufacturing, if a craft worker encounters a problem and needs to acquire typical process data, the process demand data can be input into the process knowledge integration application platform, the process knowledge integration application platform acquires the typical process data closest to the process demand data, and the craft worker can issue a process to a constructor according to the typical process data.
And S102, selecting typical process data with the typical process type same as the required process type from the typical process library.
Specifically, the typical process data includes typical process types, typical process parameter items and typical process implementation data; the typical process data can be the construction data of the process with the best construction quality in the manufacturing process of the same batch of products, or the construction data of the high-quality process which is audited by an authoritative technologist. The typical process type is that of a typical process. The typical process parameter items are process parameter items of a typical process. The process implementation data may be values of specific parameters necessary to implement a typical process, and may illustratively be weld voltage, weld current, weld speed, and/or any other parameter data that may directly instruct the operator to implement the typical process. If the typical process type of the typical process data is the same as the required process type, the typical process data and the required process data belong to the same type of process and can be selected.
And S103, calculating a final correlation value according to the typical process parameter item and the required process parameter item.
Specifically, the similarity degree of each process parameter item corresponding to the required process data and the typical process data is compared, and then the correlation value of the required process data and the typical process data is calculated according to the similarity degree of each process parameter item, wherein the similarity degree of each process parameter item is positively correlated with the correlation value. The final correlation value may represent the magnitude of the correlation between the desired process data and the representative process data.
And S104, determining target process data according to the final correlation value.
Specifically, a correlation threshold may be preset, and if the final correlation value between the typical process data and the required process data is greater than the set correlation threshold, it is determined that the typical process data is the target process data, that is, the typical process data and the required process data have extremely high correlation, and a process worker may refer to typical process implementation data in the typical process data to perform a process operation.
According to the ship manufacturing process generation method provided by the embodiment, the correlation between the typical process and the required process can be correspondingly analyzed according to the actual requirements of process personnel, and then the target process is determined according to the final correlation value, so that the target typical process can be quickly searched and accurately determined, the dependence of process design on personal experience is reduced, the typical process implementation data can directly guide the operation of constructors, the ship building quality is ensured, and the efficiency of process design and the stability of the ship building quality are improved.
With continued reference to fig. 1, optionally, both the exemplary process type and the desired process type include: the method comprises a cutting process, a machining forming process, an assembling process, a welding process, an orthopedic process and a coating process.
Fig. 2 is a flowchart of another ship manufacturing process generation method according to an embodiment of the present invention, and referring to fig. 2, the ship manufacturing process generation method includes:
s201, receiving process requirement data.
S202, selecting typical process data with the typical process type same as the required process type from the typical process library.
And S203, calculating a final correlation value according to the typical process parameter item and the required process parameter item.
Steps S201, S202, and S203 correspond to steps S101, S102, and S103, respectively, and are not described herein again.
And S204, determining the typical process data with the final correlation value larger than a preset threshold value as target process data.
Specifically, the preset threshold may be set by a user, and it may be determined whether the correlation degree between the typical process data and the required process data meets the requirement. If the final correlation value is greater than the preset threshold value, it is indicated that the correlation degree between the typical process data and the required process data meets the requirement, the typical process data can be used for guiding a constructor to execute a process or providing process implementation data meeting the requirement for the constructor, and the typical process data with the final correlation value greater than the preset threshold value is determined as target process data.
According to the ship manufacturing process generation method provided by the embodiment, the preset threshold is set, whether the typical process data meet the requirements of the process personnel is judged according to the relative relation between the final correlation value and the preset threshold, and the preset threshold can be changed according to the requirements of the process personnel, so that the typical process data are screened more purposefully, and the design efficiency of the typical process implementation data and the stability of the ship construction quality are improved.
Fig. 3 is a flowchart of a method for generating a ship manufacturing process according to another embodiment of the present invention. Referring to fig. 3, the ship manufacturing process generating method includes:
s301, receiving process requirement data.
S302, typical process data with the typical process type same as the required process type is selected from the typical process library.
Steps S301 and S302 correspond to steps S101 and S102, respectively, and are not described herein again.
S303, if the required process parameter item is of a digital type, calculating a first correlation value of the required process parameter item according to a first formula; the first formula is that ki is 1- (| Ai-Bi |/Bi), where ki is a first correlation value of the ith required process parameter item, Ai is a numerical value of the ith typical process parameter item, Bi is a numerical value of the ith required process parameter item, i is a positive integer, and i corresponds to the required process parameter item one to one.
Specifically, the digital required process parameter item may be represented by a numerical value, and for example, the cutting thickness may be represented by 10 centimeters, and the cutting thickness is the digital required process parameter item, wherein the unit of the digital required process parameter item is consistent with the unit of the corresponding typical process parameter item. If a required process parameter item is digital, the typical process parameter item corresponding to the required process parameter item is also digital. A first correlation value of the required process parameter can then be obtained according to the first formula ki ═ 1- (| Ai-Bi |/Bi).
S304, if the required process parameter item is in a character type, judging whether the content of the required process data parameter item is completely consistent with that of the corresponding typical process parameter item, if so, determining that the first correlation value is 1, otherwise, determining that the first correlation value is 0.
Specifically, the character-type required process parameter item may not be directly represented by a number, but may be represented by a character, for example, the material of the cutting object may not be represented by a number, but may be represented by a Chinese character "wood", "metal", "composite board", or other words capable of representing the material of the cutting object, and the material of the cutting object is the character-type required process parameter item. When determining the first correlation value between the character-type demand process parameter item and the corresponding typical process parameter item, it is necessary to determine whether the content of the demand process parameter item is completely consistent with the content of the corresponding typical process parameter item, if so, the first correlation coefficient is 1, otherwise, the first correlation coefficient is 0.
S305, according toCalculating a final correlation value by the first correlation value and a second formula; the second formula is
And K is the final correlation value, and xi is a preset correlation coefficient of the ith parameter item.
Specifically, after first correlation values between the required process data and all process parameter items of the typical process data are obtained, all the first correlation values are multiplied by respective preset correlation coefficients respectively and then are superposed to obtain final correlation coefficients, and the final correlation values can reflect the correlation degree between the required process data and the typical process data. The preset correlation coefficient can reflect the importance degree of the parameter item to the process, and is a preset value which can be set by a user according to actual requirements.
S306, determining the typical process data with the final correlation value larger than the preset threshold value as target process data.
Step S306 is the same as step S204, and is not described herein again.
According to the ship manufacturing process generation method provided by the embodiment, the first correlation values of the parameter items between the required process data and the typical process data are respectively calculated, the final correlation values are calculated according to the first correlation values and the preset correlation coefficients of the parameter items, and then the typical process data closest to the required process data is determined according to the final correlation values, so that the ship manufacturing typical process is accurately acquired, and the matching precision and efficiency of the typical process are improved.
Fig. 4 is a flowchart of a ship manufacturing process generation method according to an embodiment of the present invention, and referring to fig. 4, the ship manufacturing process generation method includes:
s401, receiving process requirement data.
S402, selecting typical process data with the typical process type same as the required process type from the typical process library.
Steps S401 and S402 are the same as steps S101 and S102, respectively, and are not described herein again.
And S403, determining special parameter items in the required process parameter items.
Specifically, the special parameter items are parameter items having a key effect on the required process in all required process parameter items, each process type has a unique special parameter item, when the number of the key parameter items influencing the process is more than one, the number of the special parameter items can be multiple, the special parameter items can be selected and preset by qualified process personnel, and exemplarily, the special parameter items can be welding method parameter items in a welding process, area part parameter items in a coating process and paint type parameter items.
S404, when the contents of the special parameter items are the same as those of the corresponding typical process parameter items, calculating a final correlation value according to the typical process parameter items and the required process parameter items.
Specifically, if the special parameter items of the required process data are different from the special parameter items of the typical process data, it indicates that the required process data and the typical process data have low correlation, and the typical process does not meet the requirements of the process workers and cannot guide the constructors to execute the required process, and at this time, the typical process data with different special parameter items need to be excluded and are not taken as the target process data. Furthermore, when the contents of the special parameter item and the corresponding typical process parameter item are the same, a final correlation value can be calculated according to the typical process parameter item and the required process parameter item: if the required process parameter item is a digital type, calculating a first correlation value of the required process parameter item according to a first formula, wherein the first formula is that ki is 1- (| Ai-Bi |/Bi), ki is the first correlation value of the ith required process parameter item, Ai is the numerical value of the ith typical process parameter item, Bi is the numerical value of the ith required process parameter item, i is a positive integer, and i corresponds to the required process parameter item one to one; if the required process parameter item is of a character type, judging whether the content of the required process data parameter item is completely consistent with that of the corresponding typical process parameter item, if so, the first correlation value is 1, otherwise, the first correlation value is 0; calculating a final correlation value based on the first correlation value and a second formula
And K is the final correlation value, and xi is a preset correlation coefficient of the ith parameter item.
S405, determining the typical process data with the final correlation value larger than a preset threshold value as target process data.
Step S405 is the same as step S204, and is not described herein again.
The ship manufacturing process generation method provided by this embodiment selects a special process parameter item in the required process parameter items, determines whether the required process is completely consistent with the special parameter item of the typical process, further calculates a final correlation value between the required process and the typical process according to each process parameter item if the required process is consistent with the special parameter item of the typical process, and determines target process data according to the final correlation value, thereby implementing preliminary screening of the typical process according to the special parameter item, reducing the calculation amount of the first correlation value, and improving the process determination efficiency.
Fig. 5 is a flowchart of an exemplary process library forming method according to an embodiment of the present invention, and referring to fig. 5, the exemplary process library forming method includes:
s501, receiving typical process data recorded by a process worker.
Specifically, if a process person finds that the quality monitoring data of a ship or other parts manufactured by using certain processes is obviously better than other products in the same batch in the process of manufacturing the ship, process tests or other process practice activities, the processes can be taken as typical processes and the typical process data can be recorded into a typical process library.
And S502, storing typical process data to form a typical process library.
Specifically, typical process data entered by process personnel are classified and stored to form a typical process library.
The typical process library forming method provided by the embodiment can store typical process data found by process personnel in the practical process to form a typical process library, thereby realizing reuse and sharing of process knowledge and further improving the utilization rate of a typical process.
Fig. 6 is a flowchart of another exemplary process library forming method according to an embodiment of the present invention, and referring to fig. 6, the exemplary process library forming method includes:
s601, collecting process data in the ship production construction process.
Specifically, a sensor may be disposed on a device at a ship production construction site, the sensor may acquire process data of each process in a site construction process, the process data may include a process type, a process parameter item, and process implementation data, and illustratively, the sensor may acquire a process type such as a welding process, a cutting process, an assembly process, and a coating process, a process parameter item such as a thickness, a material, and a precision requirement of a cutting object, and a corresponding specific numerical value such as a welding parameter, a cutting parameter, an assembly parameter, and a coating parameter.
And S602, collecting quality detection data corresponding to the process data.
Specifically, after the process is executed, the quality of the product is detected and quality monitoring data related to the process is collected, wherein the quality monitoring data can be weld forming effect of the product, thickness data of a coating or other data capable of reflecting process construction quality.
S603, storing the process data and the corresponding quality detection data according to the process types in a classified manner to form a process database.
Specifically, the process data and the quality detection data related to the process in the construction process of each batch of products are stored according to the process type to form a process database.
S604, extracting the process data with the best quality detection data under each process type from the process database, and forming a typical process library as typical process data.
Specifically, the process with the optimal quality detection data in each process with consistent parameter items is judged from the processes of the same batch of products, the process is used as the typical process of the batch of products, the process type, the process parameter items and the process implementation data of the process are extracted from the process database to form typical process data, and the typical process data are stored in the typical process database according to the process type.
The typical process library forming method provided by the embodiment can collect process data of each process in the ship production process, form a process database, judge the optimal process in the production process of the same batch of products as a typical process according to the quality detection data of the process, store the process data of the typical process into the typical process library, and directly guide production without determining again by a constructor, thereby realizing reuse and sharing of the typical process in the production process.
The embodiment of the invention also provides a ship manufacturing process generating device, which can implement any ship manufacturing process generating method. Fig. 7 is a schematic structural diagram of a ship manufacturing process generating apparatus according to an embodiment of the present invention, and referring to fig. 7, a ship manufacturing process generating apparatus 700 includes: the system comprises a process demand data receiving module 701, a typical process data selecting module 702, a final correlation value calculating module 703 and a target process data determining module 704, wherein the process demand data receiving module 701 is used for receiving process demand data, and the process demand data comprises a demand process type and a demand process parameter item; the typical process data selecting module 702 is configured to select typical process data in a typical process library, where the typical process data includes a typical process type, typical process parameter items, and typical process implementation data, and the typical process type is the same as a required process type; the final correlation value calculating module 703 is configured to calculate a final correlation value according to the typical process parameter item and the required process parameter item; the target process data determination module 704 is configured to determine target process data according to the final correlation value.
The ship manufacturing process generation device provided by the embodiment is provided with the process demand data receiving module, the demands of process personnel can be acquired, the typical process data selecting module can select the typical process which is the same as the demanded process, the final correlation value calculating module can calculate the final correlation values of the typical process and the demanded process according to the parameter items of the typical process and the demanded process, and the target process data determining module can determine the target process data according to the final correlation values, so that the typical process data with the highest correlation can be determined according to the demands, and the efficiency of process design and the stability of ship construction quality are improved.
Fig. 8 is a schematic structural diagram of another ship manufacturing process generating apparatus according to an embodiment of the present invention, referring to fig. 8, optionally, the final correlation value calculating module 703 includes: a first correlation value calculating unit 801 and a first final correlation value calculating unit 802, where the first correlation value calculating unit 801 is configured to calculate a first correlation value of a required process parameter item according to a first formula if the required process parameter item is a digital type, where the first formula is ki ═ 1- (| Ai-Bi |/Bi), where ki is a first correlation value of an ith required process parameter item, Ai is a numerical value of an ith required process parameter item, Bi is a numerical value of an ith typical process parameter item, i is a positive integer, and i corresponds to the required process parameter item one to one; and is used for judging whether the required process parameter item is completely consistent with the content of the corresponding typical process parameter item if the required process parameter item is a character type, if so, the first correlation value is 1, otherwise, the first correlation value is 0; the first final correlation value calculating unit 802 is configured to calculate a final correlation value according to the first correlation value and a second formula
Wherein, K is a final correlation value, xi is a preset correlation coefficient of the ith parameter item, the first correlation value calculating unit 801 and the first final correlation value calculating unit 802 realize calculation of correlation values of different types of parameter items and further calculate to obtain a final correlation value, accurate calculation of correlation values is realized, and matching between a typical process and a required process is further improved.
Fig. 9 is a schematic structural diagram of another ship manufacturing process generating apparatus according to an embodiment of the present invention, and referring to fig. 9, optionally, the final correlation value calculating module 703 includes: a special parameter item determining unit 901 and a second final correlation value calculating unit 902, wherein the special parameter item determining unit 901 is configured to determine a special parameter item in the required process parameter items; the second final correlation value calculating unit 902 is configured to calculate a final correlation value according to the typical process parameter item and the required process parameter item when the contents of the specific parameter item and the corresponding typical process parameter item are the same.
According to the ship manufacturing process determining method and device provided by the embodiment of the invention, the typical process library is established by the process with excellent quality inspection data and the method directly uploaded by experienced process personnel, and the corresponding typical process can be matched according to the required process parameters, so that the reuse and sharing of process knowledge are realized, the dependence of process design on personal experience knowledge is reduced, and the process design efficiency is improved; the similarity between the typical process and the required process is matched by inputting required process parameters, and the target process is determined according to the similarity threshold.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method of generating a ship manufacturing process, comprising:
receiving process demand data, wherein the process demand data comprises demand process types and demand process parameter items;
selecting typical process data with the typical process type same as the required process type from a typical process library, wherein the typical process data comprises the typical process type, typical process parameter items and typical process implementation data;
calculating a final correlation value according to the typical process parameter item and the required process parameter item;
and determining target process data according to the final correlation value.
2. The marine vessel manufacturing process generation method of claim 1, wherein determining whether the typical process data is target process data according to the final correlation value comprises:
determining the typical process data with the final correlation value larger than a preset threshold value as the target process data.
3. The method for generating a ship manufacturing process according to claim 1, wherein calculating a final correlation value according to the typical process parameter item and the required process parameter item comprises:
if the required process parameter item is digital, calculating a first correlation value of the required process parameter item according to a first formula, wherein the first formula is that ki is 1- (| Ai-Bi |/Bi), ki is the first correlation value of the ith required process parameter item, Ai is the numerical value of the ith typical process parameter item, Bi is the numerical value of the ith required process parameter item, i is a positive integer, and i corresponds to the required process parameter item one to one;
if the required process parameter item is of a character type, judging whether the content of the required process data parameter item is completely consistent with that of the corresponding typical process parameter item, if so, determining that the first correlation value is 1, otherwise, determining that the first correlation value is 0;
calculating a final correlation value according to the first correlation value and a second formula
And K is the final correlation value, and xi is a preset correlation coefficient of the ith parameter item.
4. The method for generating a ship manufacturing process according to claim 1, wherein calculating a final correlation value according to the typical process parameter item and the required process parameter item comprises:
determining special parameter items in the required process parameter items;
and when the contents of the special parameter item and the corresponding typical process parameter item are the same, calculating a final correlation value according to the typical process parameter item and the required process parameter item.
5. The method for generating a ship manufacturing process according to claim 1, further comprising, before receiving the process demand data:
receiving the typical process data input by a process worker;
and saving the typical process data to form the typical process library.
6. The method for generating a ship manufacturing process according to claim 5, wherein before receiving the typical process data entered by a process person, the method further comprises:
collecting process data in the ship production construction process;
collecting quality detection data corresponding to the process data;
storing the process data and the corresponding quality detection data according to process types in a classified manner to form a process database;
and extracting the process data with the best quality detection data under each process type from the process database to serve as the typical process data to form the typical process database.
7. The marine vessel manufacturing process generating method of claim 1, wherein the typical process type and the required process type each include: the method comprises a cutting process, a machining forming process, an assembling process, a welding process, an orthopedic process and a coating process.
8. A marine vessel manufacturing process creation apparatus, comprising:
the system comprises a process demand data receiving module, a processing module and a processing module, wherein the process demand data receiving module is used for receiving process demand data, and the process demand data comprises a demand process type and a demand process parameter item;
the typical process data selection module is used for selecting typical process data with the typical process type same as the required process type from a typical process library, wherein the typical process data comprises the typical process type, typical process parameter items and typical process implementation data;
the final correlation value calculating module is used for calculating a final correlation value according to the typical process parameter item and the required process parameter item;
and the target process data determining module is used for determining target process data according to the final correlation value.
9. The marine vessel manufacturing process generating apparatus of claim 8, wherein the final correlation value calculating module comprises:
a correlation value calculating unit, configured to calculate a first correlation value of the required process parameter item according to a first formula if the required process parameter item is a digital type, where the first formula is ki ═ 1- (| Ai-Bi |/Bi), where ki is the first correlation value of the ith required process parameter item, Ai is a numerical value of the ith required process parameter item, Bi is a numerical value of the ith typical process parameter item, i is a positive integer, and i corresponds to the required process parameter item one to one; and is used for judging whether the required process parameter item is completely consistent with the content of the corresponding typical process parameter item if the required process parameter item is character-type, if so, determining that the first correlation value is 1, otherwise, determining that the first correlation value is 0;
a first final correlation value calculating unit for calculating a final correlation value based on the first correlation value and a second formula
And K is the final correlation value, and xi is a preset correlation coefficient of the ith parameter item.
10. The marine vessel manufacturing process generating apparatus of claim 8, wherein the final correlation value calculating module comprises:
the special parameter item determining unit is used for determining a special parameter item in the required process parameter items;
and the second final correlation value calculating unit is used for calculating a final correlation value according to the typical process parameter item and the required process parameter item when the contents of the special parameter item and the corresponding typical process parameter item are the same.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110863379.9A CN113505890A (en) | 2021-07-29 | 2021-07-29 | Ship manufacturing process generation method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110863379.9A CN113505890A (en) | 2021-07-29 | 2021-07-29 | Ship manufacturing process generation method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113505890A true CN113505890A (en) | 2021-10-15 |
Family
ID=78015034
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110863379.9A Pending CN113505890A (en) | 2021-07-29 | 2021-07-29 | Ship manufacturing process generation method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113505890A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102069094A (en) * | 2010-11-16 | 2011-05-25 | 北京首钢自动化信息技术有限公司 | Data mining-based plate shape control key process parameter optimization system |
| WO2018103264A1 (en) * | 2016-12-06 | 2018-06-14 | 中国电子科技集团公司第三十八研究所 | Method and device for three-dimensional machining process design of common part |
| CN113177732A (en) * | 2021-05-20 | 2021-07-27 | 中船黄埔文冲船舶有限公司 | Process flow management method, device, medium and terminal equipment |
-
2021
- 2021-07-29 CN CN202110863379.9A patent/CN113505890A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102069094A (en) * | 2010-11-16 | 2011-05-25 | 北京首钢自动化信息技术有限公司 | Data mining-based plate shape control key process parameter optimization system |
| WO2018103264A1 (en) * | 2016-12-06 | 2018-06-14 | 中国电子科技集团公司第三十八研究所 | Method and device for three-dimensional machining process design of common part |
| CN113177732A (en) * | 2021-05-20 | 2021-07-27 | 中船黄埔文冲船舶有限公司 | Process flow management method, device, medium and terminal equipment |
Non-Patent Citations (2)
| Title |
|---|
| 张秀彬 等: "发明解析论", 31 May 2014, 上海交通大学出版社, pages: 408 * |
| 赵爱侠 等: "基于典型工艺的航空发动机轴类零件工艺设计方法研究", 《中国制造业信息化》, pages 42 - 46 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Roy et al. | Integrated fuzzy AHP and fuzzy TOPSIS methods for multi-objective optimization of electro discharge machining process | |
| Trung | Application of EDAS, MARCOS, TOPSIS, MOORA and PIV methods for multi-criteria decision making in milling process | |
| Wang et al. | A decision support system for additive manufacturing process selection using a hybrid multiple criteria decision-making method | |
| Sabadka et al. | Optimization of production processes using the Yamazumi method | |
| Madic et al. | Determination of manufacturing process conditions by using MCDM methods: Application in laser cutting | |
| US11170332B2 (en) | Data analysis system and apparatus for analyzing manufacturing defects based on key performance indicators | |
| CN109523188B (en) | Man-machine interface display-oriented method and system for evaluating man-machine cognitive characteristic work efficiency | |
| Datta et al. | Application of entropy measurement technique in grey based Taguchi method for solution of correlated multiple response optimization problems: A case study in welding | |
| CN112860769B (en) | Energy planning data management system | |
| CN114861120B (en) | Flotation froth grade calculation method, device, electronic equipment and medium | |
| CN109191367A (en) | The joining method of tool wear image and the life-span prediction method of cutter | |
| CN116822342A (en) | Process identification and performance prediction method for zirconium alloy laser cutting | |
| CN103069441A (en) | Dialogue system and method for examining a machining process | |
| US20230029806A1 (en) | In-situ monitoring system assisted material and parameter development for additive manufacturing | |
| CN113642666A (en) | Active enhanced soft measurement method based on sample expansion and screening | |
| Weyand et al. | Method to increase resource efficiency in production with the use of MFCA | |
| Era et al. | Prediction of mechanical behaviors of L-DED fabricated SS 316L parts via machine learning | |
| CN113505890A (en) | Ship manufacturing process generation method and device | |
| Lata et al. | Selection of machine tool by using FUZZY TOPSIS method | |
| CN111724203B (en) | Product processing price estimation method and system based on multiple regression model | |
| Wiederhold et al. | Clustering of similar processes for the application of statistical process control in small batch and job production | |
| JP2003141215A (en) | Operation analyzing device, method, computer program and computer readable storage medium, in manufacturing process | |
| CN108250342B (en) | A kind of perfluoroethylene-propylene product processes and system | |
| CN111521600A (en) | 3D printing metal component defect online monitoring and analyzing device and control method thereof | |
| CN113554336A (en) | Evaluation method of ship manufacturing process |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |